CN114251753A - Ice storage air conditioner cold load demand prediction distribution method and system - Google Patents

Abstract

The invention discloses a method and a system for predicting and distributing cold load demands of ice storage air conditioners, wherein a target building moment cold load prediction graph is established, and an ant colony is divided into a plurality of sub ant colonies; carrying out cold load demand predicted value search by utilizing each sub-ant colony, and sequencing the cold load demand predicted value search results of each sub-ant colony according to utility function values; obtaining ants corresponding to the cold load demand predicted value search result which tends to be centered in the cold load predicted value search results according to the sorting results, updating pheromones of the ants to neighbor sub-ant groups, comparing the cold load demand predicted values of the sub-ant groups at the current time of the day to be regulated and controlled, and outputting the optimal cold load demand predicted value of the current time of the day to be regulated and controlled; and (4) fitting by using a curve, and if the current moment is the last moment of the day to be regulated, obtaining the total cold load demand predicted value of the target building on the day to be regulated, so as to realize cold load demand prediction distribution. The invention improves the operation efficiency of the cold machine and obtains higher benefit.

Description

A method and system for predicting and distributing cooling load demand of ice-storage air conditioners

technical field

The invention belongs to the technical field of air conditioning and refrigeration, and in particular relates to a method and system for predicting and distributing the cooling load demand of an ice cold storage air conditioner.

Background technique

Energy problems are becoming more and more serious, and energy conservation and emission reduction has become the focus of various countries. Air conditioners are one of the most common energy-consuming equipment in buildings, accounting for about 60% of the total electricity and energy consumption. In summer, the high demand for electricity in public buildings and the power consumption of air-conditioning systems again increase the peak load of the power grid, resulting in insufficient power supply at peak times, excess trough power, and power outages. The ice storage air conditioner uses ethylene glycol aqueous solution as the refrigerant, exchanges heat with the water in the ice storage device and stores the cold, and the water stores the cooling capacity in the form of sensible heat or latent heat of phase change. During the peak period of the grid load or electricity price, the refrigeration unit and the ice storage device are used to provide cooling together, so as to realize the “peak shifting and filling valley” and energy-saving operation of the air-conditioning power consumption.

The ice storage and public transfer system is a large-scale system distributed in time and space, which balances the grid pressure and reduces the operating cost of air conditioning. However, the current centralized control structure in the application of HVAC system has problems such as complex control network construction, difficult configuration, and difficulty in implementing optimization algorithms, which makes it difficult to implement inappropriate control strategies in practical projects, resulting in low system efficiency and energy waste. etc. phenomenon. In addition, as the scale of the system increases, problems such as link congestion and operating lag occur frequently during data transmission.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to provide a method and system for predicting and distributing the cooling load demand of an ice cold storage air conditioner, aiming at the deficiencies in the above-mentioned prior art, so as to realize the minimum operating energy consumption, the lowest operating cost and the least energy loss of the ice cold storage air conditioner. Target.

The present invention adopts following technical scheme:

A method for predicting and distributing cooling load demand of an ice-storage air conditioner, comprising the following steps:

S1. Establish the cooling load prediction map of the target building at all times, set the ant colony parameters, and divide the ant colony into several sub-ant colonies;

S2. Use each sub-ant colony divided in step S1 to search for the predicted value of cooling load demand, and obtain several search results of the predicted value of cooling load demand at the current moment of the day to be controlled; Sort function values;

S3, according to the sorting result of step S2, obtain the ant corresponding to the search result of the cooling load prediction value which tends to be centered in the cooling load prediction value search result, update the pheromone of the ant to the neighbor sub-ant colony, and make several sub-ant colonies Co-evolution; after iterating the set number of times, stop the ant search, compare the predicted value of cooling load demand at the current time of the day to be regulated searched by each sub-ant colony, perform variance analysis, and output the optimal predicted value of cooling load demand at the current time of the day to be regulated. ;

S4. Fitting the optimal cooling load demand forecast value obtained in step S3 with a curve to complete the planning of the cooling load demand forecast value at the corresponding time of the target building;

S5. If the current time is the last time of the day to be regulated, stop the cycle, obtain the predicted value of the total cooling load demand of the target building on the day to be regulated, and realize the predicted distribution of cooling load demand.

Specifically, in step S1, the establishment of the cooling load prediction map of the target building time is as follows:

S101. Establish an energy consumption model of the primary side equipment for the cooling capacity of the ice storage air conditioning system, including the chiller, the cooling tower, the cooling pump and the solution pump;

S102, establishing an operating energy consumption objective function, an operating cost objective function, and an energy consumption loss objective function according to the energy consumption obtained in step S101;

S103 , establishing the constraint condition of the predicted value of the total cooling load demand of the target building, and establishing the time cooling load prediction map of the target building according to the objective function and the constraint condition.

Further, in step S101, the energy consumption model of the cooling machine is:

Among them, COP(k) is the energy efficiency ratio of the kth chiller; PLR(k) is the partial load rate of the k chiller; a ₁ , a ₂ , a ₃ , ..., a ₁₀ are the ten-term models of the chiller coefficient; T _CHWS is the chilled water supply temperature; T _CWR is the cooling water return temperature; W _c is the total energy consumption in the cooling machine operation cycle; t is the sampling time in the operation cycle; P _c (t) is the cooling machine time t Operating power; k represents the number of chillers; Q _n (k) is the rated power of the kth chiller.

The cooling tower energy consumption model is:

Among them, _ct (t) is the load of the cooling tower at t; Q _cs (t) is the cooling capacity of the chiller at t; W _ct is the total energy consumption of the cooling tower operating cycle; W _ct (t) is the cooling tower t time energy consumption; α represents a proportional coefficient;

The pump energy consumption model is:

Among them, P _CHWpump , P _CWpump and P _EGSpump are the power consumption of the freezing pump, cooling pump and solution pump respectively; ρ _w and ρ _s are the densities of the freezing water and cooling water; m _CHW , m _CW and m _EGS are the freezing water flow, Cooling water flow and ethylene glycol solution flow; H _CHW , H _CW and H _EGS represent the differential pressure, η _CHW , η _CW and η _EGS are the working efficiencies of the refrigeration pump, the cooling pump and the solution pump, respectively;

The energy consumption of cooling pump and refrigeration pump is:

The ethylene glycol solution pump operates under ice storage conditions and ice tank cooling conditions, and the energy consumption is:

Among them, m, n, j represent the number of refrigeration pumps, cooling pumps, and ethylene glycol solution pumps; t ₁ , t ₂ , and t ₃ are the ice storage time, the cooling machine working time, and the ice tank cooling time, respectively.

Further, in step S102, the running energy consumption objective function f1 is _:

f ₁ =W _T =W _c +W _ct +W _pump

Among them, _WT is the total energy consumption of the air-conditioning system during the operation cycle, Wc is the total energy consumption of the cooling machine during the operation cycle, _Wct is the total energy consumption of the cooling tower during the operation _cycle , and W _pump is the total energy consumption of the pump during the operation cycle.

Further, in step S102, the operating cost objective function f ₂ is:

Among them, W _c (t) is the energy consumption of the cooling machine t, W _ct (t) is the energy consumption of the cooling tower t, W _pump (t) is the energy consumption of the pump t, and e(t) is each sampling step. electricity price.

Further, in step S102, the energy consumption loss objective function _f3 is:

Among them, W _c (t) is the energy consumption of the cooling machine t, W _ct (t) is the energy consumption of the cooling tower t, W _pump (t) is the energy consumption of the pump t, and δ is the ice storage stage of the cooling machine. , t ₁ and t ₃ are the ice storage time and the ice tank cooling time, respectively.

Further, in step S103, the constraint conditions of the predicted value of the total cooling load demand of the target building include that the cooling capacity of the cooling machine in each time period should be less than the rated cooling capacity of the cooling machine, the total ice storage capacity in the ice storage stage is less than the ice tank capacity, and the current ice storage capacity of the ice tank. The cooling capacity at the moment is less than the remaining cooling capacity of the ice tank at the current moment, less than the maximum cooling capacity of the ice storage tank at the current moment, and the sum of the cooling capacity provided by the chiller and the ice tank should reach the accuracy range that meets the cooling load demand of the building.

Further, the cooling capacity Q(k) of the cooling machine in each time period _is less than the rated cooling capacity Qn(k) of the cooling machine, specifically:

Q(k)=Qn(k)· _PLR (k) _≤Qn (k)

In the ice storage stage, the total ice storage capacity is less than the ice tank capacity, specifically:

Q _ice.st ·0.95≤Q _{tank ≤Q ice.st}

The cooling capacity of the ice tank at the current moment is less than the remaining cooling capacity of the ice storage tank at the current moment, and less than the maximum cooling capacity of the ice storage tank at the current moment, specifically:

The range of accuracy within which the sum of the cooling provided by the chiller and the ice tank meets the cooling load requirements of the building, specifically:

|Q _c (t)+Q _tank (t)-Q _demand (t)|≤ε·Q _demand (t)

Among them, Q _ice.st is the total ice storage capacity, Q _tank is the cooling capacity of the ice tank; Q _tank (t) is the cooling capacity of the ice tank at t; h ₁ , h ₂ are fitted according to actual engineering data; Q _demand ( t) is the cooling load demand at the end of the building at t; ε is the accuracy range to meet the cooling load demand, Q _c (t) is the cooling capacity of the chiller, Q _n (k) is the rated cooling capacity, and PLR (k) is k Part load rate of the chiller.

Specifically, in step S5, if the current time is not the last time of the day to be adjusted, set the next time of the day to be adjusted equal to the current time, and return to step S2 to obtain the predicted value of cooling load demand of each sub-ant colony at the next time .

Another technical solution of the present invention is a system for predicting and distributing the cooling load demand of an ice-storage air conditioner, comprising:

Divide the modules, establish the cooling load prediction map of the target building at all times, set the ant colony parameters, and divide the ant colony into several sub-ant colonies;

The sorting module uses each sub-ant colony divided by the dividing module to search for the predicted value of cooling load demand, and obtains several search results of the predicted value of cooling load demand at the current moment of the day to be regulated; Sort by utility function value;

The analysis module, according to the sorting result of the sorting module, obtains the ants corresponding to the search results of the cooling load prediction value which tends to be centered in the search results of the cooling load prediction value, and updates the pheromone of the ants to the neighbor sub-ant colony, so that a number of sub-ants are set. Group co-evolution; after the set number of iterations, stop the ant search, compare the predicted values of cooling load demand at the current moment of the day to be regulated searched by each sub-ant colony, perform variance analysis, and output the optimal forecast of cooling load demand at the current moment of the day to be regulated. value;

The fitting module fits the optimal cooling load demand forecast value obtained by the analysis module with a curve to complete the planning of the cooling load demand forecast value at the corresponding time of the target building;

The distribution module, if the current moment is the last moment of the day to be regulated, stops the cycle, obtains the predicted value of the total cooling load demand of the target building on the day to be regulated, and realizes the predicted distribution of cooling load demand.

Compared with the prior art, the present invention at least has the following beneficial effects:

The invention provides a method for predicting and distributing the cooling load demand of an ice storage air conditioner. Under the requirement of ensuring the comfort of the end users of the target building, based on the mathematical model of the ice storage air conditioner system, the minimum operating energy consumption, the lowest operating cost, and the least energy loss are three. A parallel sorting ant colony algorithm based on cooling load demand optimization is used to control the load distribution of chillers and ice tanks in the ice storage air conditioning system according to the optimization results. The multi-objective optimal control based on constraints makes the ice storage air conditioning system not only ensure the quality of the indoor environment, but also meet the requirements of energy-saving and economical operation; based on the parallel sorting ant colony algorithm, in the genetic algorithm, in order to improve the algorithm search speed, sorting The higher the individual fitness, the higher the ranking, and the higher the probability of being selected next time. The concept of genetic algorithm is extended to the parallel sorting ant colony algorithm, that is, after all ants complete one iteration, select the w-1 ants with the top contribution ranking in the ant colony and the ants that constitute the optimal solution so far, only for The pheromone of the path constructed by these w ants is updated.

Further, the ant colony algorithm is used to collect the predicted value of the cooling load at the current moment of the day to be regulated, and the pheromone of the ants is updated to the neighboring sub-ant colonies, so that several sub-ant colonies co-evolve; after the set number of iterations, the ant search is stopped, Compare the predicted value of cooling load demand at the current moment of the day to be regulated searched by each sub-ant colony, perform variance analysis, and output the optimal predicted value of cooling load demand at the current moment of the day to be regulated; if the current moment is not the last moment of the day to be regulated, Then make the next moment of the day to be regulated equal to the current moment and execute the cycle to obtain the forecast value of cooling load demand at the next moment; if the current moment is the last moment of the day to be regulated, stop the cycle and obtain the forecast of cooling load demand at all times The total cooling load demand forecast value of the target building on the day to be regulated is obtained by using the spanning tree summation method. The parallel ant colony algorithm is efficient, which can divide the ant colony into sub-ant colonies and search for the predicted value of cooling load at different times at the same time.

Further, if the cooling capacity distribution result satisfies the minimum energy consumption, cost and energy loss of the target building, it means that the three objective functions of the operation energy consumption objective function, the operation cost objective function and the energy consumption loss objective function are the smallest.

Further, the operating energy consumption is the total energy consumption of the air-conditioning system in the operation period, and also the sum of the total energy consumption in the cooling machine operation period, the total energy consumption in the cooling tower operation period and the total energy consumption in the pump operation period.

Further, the operating cost is the operating energy consumption at different times in 24 hours and the electricity price of each sampling step at this time.

Further, the energy loss is the sum of the energy consumed by the cooling tower and the chiller when the ice storage stage is converted into cold energy and the energy consumption of the pump in the ice storage stage and the ice tank cooling stage.

Further, the cooling capacity distribution result also needs to satisfy: the cooling capacity of the cooling machine in each time period is less than or equal to the rated cooling capacity of the cooling machine; the cooling capacity of the ice tank during the operation period is less than or equal to the total ice storage capacity of the ice tank and greater than or equal to 95% of the total ice storage capacity; the cooling capacity of the ice tank at the current moment is less than or equal to the remaining cooling capacity of the ice storage tank at the current moment and the maximum cooling capacity of the ice storage tank at the current moment.

Further, when the ant colony algorithm seeks the predicted value of the cooling load at a certain time, if the current time is not the last time of the day to be regulated, then the next time of the day to be regulated is equal to the current time and returns to step S2 to obtain the next time. If the current moment is the last moment of the day to be regulated, stop the cycle, obtain the forecast value of cooling load demand at all times, and use the spanning tree sum method to obtain the total cooling load of the target building on the day to be regulated. demand forecast.

To sum up, under the requirement of ensuring the comfort of the end users of the target building, the present invention is based on the mathematical model of the ice-storage air-conditioning system, and takes the minimum system operation energy consumption, the minimum operation cost, and the minimum energy consumption loss as the three objectives, and is based on the parallel ants. The variance analysis of the swarm algorithm is used to optimize it, and the load distribution of the chiller unit and the ice tank of the ice storage air conditioning system is controlled according to the optimization result. The multi-objective optimal control based on constraints makes the ice-storage air-conditioning system not only ensure the quality of the indoor environment, but also meet the requirements of energy-saving and economical operation.

The technical solutions of the present invention will be further described in detail below through the accompanying drawings and embodiments.

Description of drawings

Fig. 1 is the decentralized control structure diagram of the ice storage air conditioning system;

Fig. 2 is the flow chart that each sub-ant colony carries out the optimal solution search process;

Figure 3 is a schematic diagram of the load rate of the chiller/ice tank;

Figure 4 is a cooling distribution diagram;

Figure 5 is a schematic diagram of the iterative process of the parallel sorting ant colony algorithm, wherein (a) is the running energy consumption, (b) is the running cost, and (c) is the energy loss;

Figure 6 is a schematic diagram of the optimal solution set distribution of the parallel sorting ant colony.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

In the description of the present invention, it is to be understood that the terms "comprising" and "comprising" indicate the presence of the described features, integers, steps, operations, elements and/or components, but do not exclude one or more other features, The existence or addition of a whole, step, operation, element, component, and/or a collection thereof.

It should also be understood that the terminology used in the present specification is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural unless the context clearly dictates otherwise.

It should further be understood that, as used in this specification and the appended claims, the term "and/or" refers to and including any and all possible combinations of one or more of the associated listed items .

Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. The figures are not to scale, some details have been exaggerated for clarity, and some details may have been omitted. The shapes of various regions and layers shown in the figures and their relative sizes and positional relationships are only exemplary, and in practice, there may be deviations due to manufacturing tolerances or technical limitations, and those skilled in the art should Regions/layers with different shapes, sizes, relative positions can be additionally designed as desired.

Parallel sorting ant colony algorithm: The ant colony can always find the shortest path to the food source in different environments. This is an intelligent behavior that the ant colony will manifest, because the ants will release a A substance called "pheromone", the sensory cells of individual ants in the ant colony have receptors that bind to pheromone, thus affecting the behavior of individual ants, tending to choose a path with a higher concentration of pheromone, and every passing ant will Leaving pheromones, the whole process is a positive feedback process. As the number of generations selected by the ant colony increases, all ants will finally choose one path, which is the optimal solution from the ant nest to the food source. path. Italian scholars Dorigo and others were inspired by this ant foraging behavior and proposed an ant colony algorithm to solve various combinatorial optimization problems.

(1) Ant transition probability

The way each ant in the ant colony chooses the next forward position is the way of roulette. When building a path, ant k chooses the next forward direction according to the node selection probability. When ant k is at node i, node j is selected. The probability of (city not visited) is:

其中τ_ij是边(i,j)上的信息素，μ_ij是边(i,j)所具有的启发式信息，对于一般的路径搜素问题，μ_ij取路径长度的倒数，α和β为算法参数。If the node has already been visited,

where τ _ij is the pheromone on the edge (i, j), μ _ij is the heuristic information of the edge (i, j), for the general path search problem, μ _ij takes the reciprocal of the path length, α and β are the algorithm parameters.

(2) pheromone update rules

After completing a path construction, the ant needs to perform a pheromone update. First, a part of all pheromones on the path volatilize, and then the corresponding pheromone is released on the traversed path. The rules for pheromone volatilization are:

τ _ij ←(1-ρ)τ _ij

Among them, ρ is the pheromone volatilization factor, 0<ρ<1. After the pheromone volatilizes, the ants release the pheromone. The update formula of the pheromone is:

是第k只蚂蚁在其经过的路径上释放的信息素的量。当边(i,j)在蚂蚁k构建的路径上，

而当边(i,j)不在蚂蚁k构建的路径上，

in,

is the amount of pheromone released by the kth ant on its path. When edge (i, j) is on the path constructed by ant k,

And when edge (i, j) is not on the path constructed by ant k,

Please refer to Figure 1. The decentralized control structure of the ice storage air conditioning system is composed of refrigeration units, ice storage devices, connecting pipes and adjustment controllers. It is mainly divided into cold source structure, chilled water structure, cooling water structure and control structure.

The cold-source structure of ice-storage air-conditioning consists of a dual-mode refrigeration unit and an ice-storage device. The dual-mode refrigeration unit uses electricity to make ice at night. After the ethylene glycol aqueous solution is cooled by the refrigeration unit, it is pressurized by the solution pump and transported to the coil of the ice storage device to exchange heat with the water in the ice tank. The alcohol aqueous solution is returned to the refrigeration unit to cool down again. The next day, the dual-mode refrigeration unit uses the air-conditioning mode for cooling. The refrigerant in the refrigeration unit exchanges heat with the chilled water to cool the chilled water, and the chilled water pump sends the chilled water to the heat exchanger. The refrigeration unit cools down again. The ice storage device adopts a layer of thermal insulation material to isolate the cold and heat exchange with the outside world and maintain the temperature of the internal energy storage medium. When the ice tank melts ice for cooling, the return water of the chilled water returns to the ice tank through the freezing pump, and exchanges heat with the low-temperature ice in the ice tank for cooling. The ice tank is different from the refrigerator and cannot control the constant outlet water temperature. Its thermal capacity is mainly determined by the structure and material of the ice tank, the heat exchange area, and the temperature and flow rate of the solution at the entrance of the ice tank. Control the rate of ice melting.

The cooling water structure includes cooling pump, cooling tower and cooling water pipeline. The unit with dual working conditions provides low-temperature refrigerant to cool down the chilled water, the cooling water absorbs the heat released by it, and the temperature of the cooling water rises. It is pressurized by the cooling water pump and sent to the cooling tower for cooling and cooling, and then flows into the cooling tower water tray, so the reciprocating cycle takes away the heat exchange of the refrigeration unit.

The control structure is the basis for the economical and energy-saving operation of the ice-storage air conditioner. In addition to the start-stop control of the mechanical and electrical equipment of the system, the control structure should also combine the peak and valley electricity price and the cooling load demand of the next day to control and adjust the working state of the refrigeration unit in the ice-making condition, and the cooling between the refrigeration unit and the ice storage device in the cooling condition. Quantity distribution, as well as the working state of the refrigeration unit under cooling conditions, to achieve economical and energy-saving benefits. The control system consists of communication systems, sensors, controls and actuators.

Referring to FIG. 2 , a method for predicting and distributing the cooling load demand of an ice storage air conditioner according to the present invention includes the following steps:

S1. Establish the cooling load prediction map of the target building at all times, set the ant colony parameters, and divide the ant colony into several sub-ant colonies;

According to the energy consumption, the objective function is established as follows:

S101. Establish an energy consumption model of the primary side equipment for the cooling capacity of the ice storage air conditioning system, including the chiller, the cooling tower, the cooling pump and the solution pump;

Cooler energy consumption model:

Among them, COP(k) is the energy efficiency ratio of the kth chiller; PLR(k) is the partial load rate of the k chiller; a ₁ , a ₂ , a ₃ , ..., a ₁₀ are the ten-term models of the chiller coefficient; T _CHWS is the chilled water supply temperature, °C; T _CWR is the cooling water return water temperature, °C; W _c is the total energy consumption in the cooling machine operating cycle, kkk; t is the sampling time in the operating cycle, sampling once per hour, k; P _c (t) is the operating power of the chiller at time t, kk; k represents the number of chillers; Q _n (k) is the rated power of the kth chiller, kk.

Cooling tower energy consumption model:

Among them, _ct (t) is the load of the cooling tower at t, kk; Q _cs (t) is the cooling capacity of the chiller at t, kk; W _ct is the total energy consumption of the cooling tower operating cycle, kkk; W _ct ( t) is the energy consumption of the cooling tower at t, kkk; α represents the proportional coefficient, which is obtained by fitting according to the actual engineering data; the meanings of other parameters are the same as before.

Pump energy consumption model:

Among them, P _CHWpump , P _CWpump and P _EGSpump are the power consumption of refrigeration pump, cooling pump and solution pump respectively, kk; w is the density of chilled water and cooling water, kg/m ³ ; m _CHW , m _CW and m _EGS are chilled water Flow rate, cooling water flow rate and ethylene glycol solution flow rate, m ³ /k; H _CHW , H _CW and H _EGS represent the differential pressure, kPa; CHW, CW and EGS represent the working efficiency of these three types of pumps, where the differential pressure and The pump working efficiency is given by equations (13) to (19).

H _CHW = b ₀ m _CHW ² +b ₁ wm _CHW +b ₂ w ² (13)

η _CHW = c ₀ (m _CHW /w) ² +c ₁ (m _CHW /w) + c ₂ (14)

H _CW =d ₀ m _CW ² +d ₁ wm _CW +d ₂ w ² (15)

η _CW =e ₀ (m _CW /w) ² +e ₁ (m _CW /w)+e ₂ (16)

H _EGS = f ₀ m _EGS ² +f ₁ wm _EGS +f ₂ w ² (17)

η _EGS = g ₀ (m _EGS /w) ² +g ₁ (m _EGS /w) + g ₂ (18)

The speed ratio w of the pump is shown in formula (14), n ₀ is the rated speed of the pump, r/min; n is the actual speed under working conditions, r/min; b ₀ , b ₁ , b ₂ ,..., g ₀ , g ₁ , g ₂ parameters are obtained according to actual engineering fitting; other parameters have the same meaning as before. The working time of the cooling pump and the freezing pump is the same as the working time of the chiller, that is, the ice storage condition and the chiller cooling condition. The energy consumption of the cooling pump and the freezing pump is shown in equations (20) and (21). The ethylene glycol solution pump operates under the ice storage condition and the ice tank cooling condition, and the energy consumption is shown in formula (22).

Among them, m, n, j represent the number of refrigeration pumps, cooling pumps, and ethylene glycol solution pumps; t ₁ , t ₂ , and t ₃ are the ice storage time, the cooling machine working time, and the ice tank cooling time, k.

S102. Establish an objective function

The ice storage air conditioning system is designed to solve the problems of energy utilization, balancing the power grid, and saving costs. Increasing the amount of ice storage at nighttime low electricity prices can effectively reduce operating costs, but the multi-layer physical state conversion and heat dissipation in the ice storage process will inevitably lead to energy loss. Reduce the amount of ice storage, that is, face increased operating costs. Therefore, in order to improve its energy utilization efficiency and minimize system energy loss and operating costs, the present invention takes the minimum operating energy consumption, the lowest operating cost, and the lowest energy loss as the objective function to optimize each time period in the operation cycle of the ice storage air conditioning system. cooling strategy.

The three objective functions of operation energy consumption objective function, operation cost objective function and energy consumption loss objective function are the smallest

1) Running the energy consumption objective function f ₁ is as follows:

f ₁ =W _T =W _c +W _ct +W _pump (23)

Among them, _WT is the total energy consumption of the air-conditioning system during the operation cycle, Wc is the total energy consumption of the cooling machine during the operation cycle, _Wct is the total energy consumption of the cooling tower during the operation _cycle , and W _pump is the total energy consumption of the pump during the operation cycle.

2) Operational cost objective function

The operating cost of the ice storage air-conditioning system in the operating cycle, that is, the sum of the product of the total energy consumption at each time of the operating cycle and the electricity price at the corresponding time:

Among them, Cost is the total operating cost of the ice storage air conditioning system in the operation cycle, yuan; e(t) is the electricity price of each sampling step, yuan; the meanings of other parameters are the same as before.

3) Energy loss objective function

In the ice storage stage of the ice storage system, the chiller provides cold energy for ice storage. During the ice storage process, the thermal resistance increases with the thickening of the ice layer, the heat exchange weakens, and the ice storage capacity of the chiller decreases, resulting in partial energy loss. Ignoring the effect of the heat loss formed by the heat exchange between the ice groove and the air, it is considered that the cooling capacity conversion rate of the ice storage in the chiller is δ in the ice storage stage. Compared with traditional air conditioners, the ice storage system increases the transfer and transfer of cooling capacity, and the energy consumption of the solution pump during the ice storage stage and the ice tank cooling stage is also included as part of the energy loss.

Among them, δ is the cooling capacity conversion rate of ice storage by the chiller in the ice storage stage, and t ₁ and t ₃ are the ice storage time and the ice tank cooling time, respectively.

S103. The constraints for establishing the predicted value of the total cooling load demand of the target building include:

1) The cooling capacity of the chiller in each period should be less than the rated cooling capacity of the chiller;

Q(k)=Qn(k)· _PLR (k) _≤Qn (k) (26)

2) The total ice storage capacity in the ice storage stage should be less than the ice tank capacity, and in order to prevent the phenomenon of "10,000-year-old ice", the cooling capacity of the ice tank during the operation period should be less than the total ice storage capacity of the ice tank, and greater than 95% of the total ice storage capacity. %;

Q _ice.st ·0.95≤Q _{tank ≤Q ice.st} (27)

3) The cooling capacity of the ice tank at the current moment is less than the remaining cooling capacity of the ice storage tank at the current moment, and less than the maximum cooling capacity of the ice storage tank at the current moment;

4) In order to meet the indoor comfort requirements of the building and ensure energy saving, the sum of the cooling capacity provided by the chiller and the ice tank should reach the accuracy range that meets the cooling load requirements of the building.

|Q _c (t)+Q _tank (t)-Q _demand (t)|≤ε·Q _demand (t) (29)

Among them, Q _tank is the cooling capacity of the ice tank, kk; Q _tank (t) is the cooling capacity of the ice tank at time t, kk; h ₁ , h ₂ are fitted according to actual engineering data; Q _demand (t) is the building at time t The cooling load demand at the end of the object, kk; ε is the accuracy range to meet the cooling load demand, and Q _c (t) is the cooling capacity of the chiller.

For the specific results, please refer to Figure 5 and Figure 6. The analysis experiment was carried out according to a large number of parameters of the ice storage air conditioner in a shopping mall in Xi'an. It is determined that the sub-ant colony size is set to 50, the maximum number of iterations is 200 generations, and the path pheromone update cycle is 20 generations. After each iteration of the parallel sorting ant colony algorithm is completed, the path pheromone is sorted according to the contribution value of the ants in the current sub-ant colony. diversity.

The parallel sorting ant colony algorithm was used to conduct experiments on a large number of parameters of ice storage air conditioners in a shopping mall in Xi'an, to solve the hourly partial load rate of the chiller and the cooling ratio of the ice tank. Figure 6 is the optimal solution distribution diagram.

Figure 5 shows the evolution process of the three objectives of the parallel sorting ant colony algorithm (operating energy consumption, operating cost, energy consumption loss). After that, the three targets tend to be stable.

S2. Use each sub-ant colony divided in step S1 to search for the predicted value of cooling load demand, and obtain several search results of the predicted value of cooling load demand at the current moment of the day to be controlled; Sort function values;

S3. Obtain the better ants according to the sorting results of S2, (that is, the ants corresponding to the search results of most cooling load prediction values in the utility function value sorting, tend to the ants corresponding to the search results of a relatively middle cooling load demand prediction value), and compare the The pheromone of the superior ant is updated to the neighbor sub-ant colony, so that several sub-ant colonies co-evolve; after the iteration is set for a set number of times, the ant search is stopped, and the predicted value of the cooling load demand at the current time of the day to be regulated searched by each sub-ant colony is compared. Perform variance analysis, and output the optimal cooling load demand forecast value at the current moment of the day to be regulated;

S4, using curve fitting between the optimal cooling load demand forecast values at different times of the day to be regulated output in step S3, that is, to complete the planning of the cooling load demand forecast value of the target building at this moment;

S5. If the current time is not the last time of the day to be regulated, make the next time of the day to be regulated equal to the current time, and return to step S2 to obtain the predicted value of cooling load demand of each sub-ant colony at the next time; if the current time is At the last moment of the day to be regulated, the cycle is stopped, and the predicted value of cooling load demand at all times of the ant colony is obtained, and then the predicted value of the total cooling load demand of the target building on the day to be regulated is obtained.

In still another embodiment of the present invention, a system for predicting and distributing the cooling load demand of an ice cold storage air conditioner is provided, and the system can be used to realize the above-mentioned method for predicting and distributing the cooling load demand of an ice cold storage air conditioner. The system includes a division module, a sorting module, an analysis module, a fitting module and an assignment module.

Among them, the module is divided, the cooling load prediction map of the target building is established, the ant colony parameters are set, and the ant colony is divided into several sub-ant colonies;

The sorting module uses each sub-ant colony divided by the dividing module to search for the predicted value of cooling load demand, and obtains several search results of the predicted value of cooling load demand at the current moment of the day to be regulated; Sort by utility function value;

The analysis module, according to the sorting result of the sorting module, obtains the ants corresponding to the search results of the cooling load prediction value which tends to be centered in the search results of the cooling load prediction value, and updates the pheromone of the ants to the neighbor sub-ant colony, so that a number of sub-ants are set. Group co-evolution; after the set number of iterations, stop the ant search, compare the predicted values of cooling load demand at the current moment of the day to be regulated searched by each sub-ant colony, perform variance analysis, and output the optimal forecast of cooling load demand at the current moment of the day to be regulated. value;

The fitting module fits the optimal cooling load demand forecast value obtained by the analysis module with a curve to complete the planning of the cooling load demand forecast value at the corresponding time of the target building;

The distribution module, if the current moment is the last moment of the day to be regulated, stops the cycle, obtains the predicted value of cooling load demand at all times of the ant colony, obtains the predicted value of total cooling load demand of the target building on the day to be regulated, and realizes the cooling load demand Forecast distribution.

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments. The components of the embodiments of the invention generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations. Thus, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

An ice storage air conditioning load distribution optimization method, according to the parameter model of the main equipment of the ice storage air conditioning system, based on the dynamic electricity price and equipment operation constraints, considering the demand-response ice storage and ice melting scheduling, with daily operating energy consumption, operating cost and energy consumption. The consumption loss is the optimization target, and the power consumption model of the chiller, cooling tower and pump electromechanical equipment of the ice-storage air-conditioning system is established. And a parallel sorting ant colony algorithm is proposed to solve the hourly load rate of the chiller and the hourly cooling ratio of the ice tank. The algorithm includes k+1 nodes connected, and each node executes the optimization adjustment task based on the parallel sorting ant colony algorithm in turn to control the corresponding cooling machine or ice tank. The control is obtained after the optimization adjustment task is performed for each node. Feasible solution set, select any group in the feasible solution set to set the cooling ratio of the ice tank in the target building and the cooling capacity of k chillers.

For each node, all ants in each sub-ant colony in the node first construct the cooling load distribution ratio once, and then the pheromone volatilizes; The cooling load distribution ratio of the ants with the optimal solution so far releases pheromone, and then receives the information of the neighbor sub-ant colony, allowing the ants with higher contribution from the neighbor sub-ant colony to release the pheromone in the current sub-ant colony; after that, the maximum value is reached. After the number of iterations, the optimal cooling load distribution ratio is output, and the global optimal cooling load distribution ratio is selected for output according to the search results of each sub-ant colony.

The parallel sorting ant colony algorithm includes the following sub-steps:

1. All ants in each sub-ant colony build a path separately;

2. Perform pheromone volatilization and update the pheromone of each sub-ant colony path;

Specifically, according to the ranking of the contribution values of the ants in the current sub-ant colony, the ants in the front of the ranking and the paths of the ants that have generated the optimal solution so far are selected, and the path pheromone is released; then the neighbor sub-ant colony path information is received, and the neighbor sub-ants are allowed to Ants with higher group contribution release path pheromone in this sub-ant colony;

After the set number of iterations, the optimal path is output, and each path search result is obtained.

In step 2, after each ant generates a path, before updating the path pheromone, volatilize the pheromone that existed on the path before. The volatilization formula is as follows:

τ _ij ←(1-ρ)τ _ij

Among them, ρ is the information volatility factor, and τ _ij is the path pheromone on the edge (i, j).

In step 2, in the process of constructing the cooling load ratio of the target building of the ant, when the ant falls into a dead angle state, the path constructed by the ant is not considered.

Note: The dead-end state refers to the deviation of individual ants from the path constructed by the majority of ants.

The path refers to the cooling load distribution ratio of the target building established by the ants.

In this example, a shopping mall in Xi'an is used as the model verification experimental environment. The ice storage air-conditioning system of the shopping mall uses 3 centrifugal chillers with dual working conditions, the rated power is 4430kk, and the ten model coefficients of the chillers are given in Table 1; The total ice storage capacity of the ice melting coil is 73000kk, which is designed according to 30% of the typical design daily cooling load requirement; each of the refrigeration pump, cooling pump and solution pump is 3 sets of equipment with the same specifications and models, with a rated power of 160kk and a rated speed of 1450r/min. The proportional coefficient α of the power consumption of the cooling tower and its load, the model coefficients of the pump b0,b1,b2,....g0,g1,g2, the ice storage rate δ of the cooling capacity of the chiller under the ice storage condition, the ice melting for cooling The parameters k1, k2, the accuracy range ε of the shopping mall's cooling to meet the cooling load demand, are obtained by fitting the historical data of the shopping mall's energy consumption collection system. The detailed model parameters are shown in Table 1. At present, the ice storage system of the shopping mall adopts the component cooling mode and the proportional control strategy, and the ice tank and the chiller share the cooling load demand in proportion to each sampling step. Table 2 shows the time-of-use electricity price in Xi'an.

Table 1 Model parameters

Table 2 Time-of-use electricity price table in Xi'an

Using the grey correlation analysis method, the current outdoor air temperature, the current solar radiation intensity, the current outdoor wind speed, the current relative humidity, the outdoor air temperature at the previous moment, the cooling load at the previous moment, etc. Based on the sample data of the factors, the strength, magnitude and order of the influence of each factor on the cooling load results were statistically analyzed. The gray correlation degree of each influencing factor and the air-conditioning cooling load at time T is shown in Table 3;

Table 3. Grey correlation between each influencing factor and air conditioning cooling load at time T

The ice storage time is set from 00:00 at night to 08:00 in the morning, a total of 8 hours, and the cooling time is from 08:00-24:00, a total of 16 hours. Taking the partial load rate PLRt(k) of the k-th chiller at t and the proportion of ice tank cooling to the current cooling load demand tank.c(t) at t as decision variables, the 24 row vector groups of matrix A and the matrix The 9 to 24 rows of B are 16 row vector groups β, which form an N-dimensional decision variable. According to the variation law of the COP of the chiller and its PLR, when the PLR is below 0.3, the cooling capacity of the chiller is low and the energy consumption is high. Therefore, the upper and lower boundary values of the row vector group α are set to 0.3 and 1. The value range of the group is [0,1].

x _i =[α ₁ ,α ₂ ,...,α ₂₄ ,β ₉ ,...,β ₂₄ ] (30)

Feasible solutions set decision variables _xi to calculate the operating energy consumption, operating cost and energy loss of the ice storage system in the operating cycle. Among them, the equipment parameters such as the ten coefficients of the cooling machine, the rated power of the cooling machine, the rated power of the pump and the rated speed of the pump are provided by the equipment supplier. The parameters are obtained from actual engineering data, and the ice storage time, the cooling time of the chiller and the cooling time of the ice tank are calculated from the decision variables.

According to the relevant parameters of the ice storage air conditioning system in the shopping mall, the hourly cooling load forecast data on July 12, 2017 was selected to optimize the ice storage at night, and the load distribution between the chiller and the ice tank on the next day was optimized. After the algorithm iteration is terminated, a set of solutions with the smallest crowding distance is selected from the optimal solution set. Figure 3 shows the distribution of the optimal solution set of the optimization algorithm. Figure 3 shows the part of each cooling machine per hour of this set of solutions during the operation cycle. The load rate and the proportion of the cooling capacity of the ice tank during the cooling time to the cooling load demand at the current moment. According to the mathematical model of ice storage air conditioning system, the total ice storage capacity is 68986.6kw, which is 36% of the cooling load demand of the building on the next day, and the total cooling supply of the ice tank is 67710.35kw, which is 98.15% of the total ice storage capacity, which meets the design requirements. . The hourly part load rate of the chiller fluctuates at the highest point of COP of 0.85, and the operation efficiency is high. Figure 4 shows the distribution of ice storage capacity, chiller and ice tank cooling capacity in each sampling step at night in the solution, and in the cooling stage, the errors of the total cooling capacity and demand of the ice tank and refrigeration unit are all in an ideal state.

To sum up, the present invention provides a method and system for predicting and distributing the cooling load demand of an ice-storage air-conditioning system. Taking the energy consumption, operating costs and energy loss of the ice-storage air-conditioning system as the optimization goals, the hourly load rate of the chiller and the ice-slot change-over-rate are calculated. cooling ratio. Through the method, the optimization result improves the operation efficiency of the chiller, and the load distribution between the chiller and the ice tank balances the contradiction between the system operation energy consumption and the operation cost, and obtains higher benefits.

As will be appreciated by those skilled in the art, the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

The above content is only to illustrate the technical idea of the present invention, and cannot limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention all fall within the scope of the claims of the present invention. within the scope of protection.

Claims (10)

1. A method for predicting and distributing the cooling load demand of an ice-storage air conditioner, characterized in that it comprises the following steps: S1. Establish the cooling load prediction map of the target building at all times, set the ant colony parameters, and divide the ant colony into several sub-ant colonies; S2. Use each sub-ant colony divided in step S1 to search for the predicted value of cooling load demand, and obtain several search results of the predicted value of cooling load demand at the current moment of the day to be regulated; Sort function values; S3, according to the sorting result of step S2, obtain the ant corresponding to the search result of the cooling load prediction value which tends to be in the center in the cooling load prediction value search result, update the pheromone of the ant to the neighbor sub-ant colony, and make several sub-ant colonies Co-evolution; after the set number of iterations, stop the ant search, compare the predicted value of cooling load demand at the current time of the day to be regulated searched by each sub-ant colony, perform variance analysis, and output the optimal predicted value of cooling load demand at the current time of the day to be regulated. ; S4. Fitting the optimal cooling load demand forecast value obtained in step S3 with a curve to complete the planning of the cooling load demand forecast value at the corresponding time of the target building; S5. If the current moment is the last moment of the day to be regulated, stop the cycle, obtain the predicted value of the total cooling load demand of the target building on the day to be regulated, and realize the predicted distribution of cooling load demand. 2. The method for predicting and distributing the cooling load demand of an ice cold storage air conditioner according to claim 1, wherein in step S1, establishing a cooling load prediction map at the time of the target building is specifically: S101. Establish an energy consumption model of the primary side equipment for cooling capacity provision of an ice-storage air-conditioning system, including a chiller, a cooling tower, a cooling pump and a solution pump; S102, establishing an operating energy consumption objective function, an operating cost objective function and an energy consumption loss objective function according to the energy consumption obtained in step S101; S103 , establishing the constraint condition of the predicted value of the total cooling load demand of the target building, and establishing the time cooling load prediction map of the target building according to the objective function and the constraint condition. 3. The method for predicting and distributing the cooling load demand of an ice-storage air conditioner according to claim 2, wherein in step S101, the energy consumption model of the chiller is: COP(k)=a ₁ +a ₂ ·PLR(k)+a ₃ ·T _CHWS +a ₄ ·T _CWR +a ₅ ·PLR(k) ² +a ₆ ·T _CHWS ² +a ₇ ·T _CWR ² +a ₈ ·PLR(k) ·T _CHWS +a ₉ ·PLR(k) ·T _CWR +a ₁₀ ·T _CHWS ·T _CWR

Among them, COP(k) is the energy efficiency ratio of the kth chiller; PLR(k) is the partial load rate of the k chiller; a ₁ , a ₂ , a ₃ , ..., a ₁₀ are the ten-term models of the chiller coefficient; T _CHWS is the chilled water supply temperature; T _CWR is the cooling water return temperature; W _c is the total energy consumption in the cooling machine operation cycle; t is the sampling time in the operation cycle; P _c (t) is the cooling machine time t Operating power; k represents the number of chillers; Q _n (k) is the rated power of the kth chiller; The cooling tower energy consumption model is:

Among them, _ct (t) is the load of the cooling tower at t; Q _cs (t) is the cooling capacity of the chiller at t; W _ct is the total energy consumption of the cooling tower operating cycle; W _ct (t) is the cooling tower t time energy consumption; α represents a proportional coefficient; The pump energy consumption model is:

in,

and

are the power consumption of the freezing pump, cooling pump and solution pump, respectively; _ρw and _ρs are the densities of chilled water and cooling water; m _CHW , m _CW and m _EGS are the chilled water flow, cooling water flow and ethylene glycol solution flow ; H _CHW , H _CW and H _EGS represent the differential pressure, η _CHW , η _CW and η _EGS are the working efficiencies of the refrigeration pump, the cooling pump and the solution pump, respectively; The energy consumption of cooling pump and refrigeration pump is:

The ethylene glycol solution pump operates under the ice storage condition and the ice tank cooling condition, and the energy consumption is:

Among them, m, n, j represent the number of refrigeration pumps, cooling pumps, and ethylene glycol solution pumps; t ₁ , t ₂ , and t ₃ are the ice storage time, the cooling machine working time, and the ice tank cooling time, respectively. 4. The method for predicting and distributing the cooling load demand of an ice cold storage air conditioner according to claim ₂ , wherein in step S102, the operating energy consumption objective function f1 is: f ₁ =W _T =W _c +W _ct +W _pump Among them, _WT is the total energy consumption of the air-conditioning system during the operation cycle, Wc is the total energy consumption of the cooling machine during the operation cycle, _Wct is the total energy consumption of the cooling tower during the operation _cycle , and W _pump is the total energy consumption of the pump during the operation cycle. 5. The method for predicting and distributing the cooling load demand of an ice-storage air conditioner according to claim ₂ , wherein in step S102, the operating cost objective function f2 is:

Among them, W _c (t) is the energy consumption of the cooling machine t, W _ct (t) is the energy consumption of the cooling tower t, W _pump (t) is the energy consumption of the pump t, and e(t) is each sampling step. electricity price. 6. The method for predicting and distributing the cooling load demand of an ice storage air conditioner according to claim 2, wherein in step S102, the energy loss objective function _f3 is:

Among them, W _c (t) is the energy consumption of the chiller at t, W _ct (t) is the energy consumption of the cooling tower at t, W _pump (t) is the energy consumption of the pump at t, and δ is the ice storage stage of the chiller. , t ₁ and t ₃ are the ice storage time and the ice tank cooling time, respectively. 7 . The method for predicting and distributing the cooling load demand of an ice storage air conditioner according to claim 2 , wherein in step S103 , the total cooling load demand prediction value constraint condition of the target building includes that the cooling capacity of the cooling machine in each time period should be less than that of the cooling machine. 8 . The rated cooling capacity of the ice storage stage, the total ice storage capacity in the ice storage stage is less than the capacity of the ice tank, the cooling capacity of the ice storage tank at the current moment is less than the remaining cooling capacity of the ice storage tank at the current moment, less than the maximum cooling capacity of the ice storage tank at the current moment, and the cooling machine and the ice storage tank. The sum of the cooling provided shall be within the accuracy range to meet the cooling load requirements of the building. 8. The method for predicting and distributing the cooling load demand of an ice cold storage air conditioner according to claim 7, wherein the cooling capacity Q(k) of each time period of the chiller _is less than the rated cooling capacity Qn(k) of the chiller, specifically: Q(k)=Qn(k)· _PLR (k) _≤Qn (k) In the ice storage stage, the total ice storage volume is less than the ice tank capacity. Specifically: Q _ice.st ·0.95≤Q _{tank ≤Q ice.st} The cooling capacity of the ice tank at the current moment is less than the remaining cooling capacity of the ice storage tank at the current moment, and less than the maximum cooling capacity of the ice storage tank at the current moment, specifically:

The range of accuracy within which the sum of the cooling provided by the chiller and the ice tank meets the cooling load requirements of the building, specifically: |Q _c (t)+Q _tank (t)-Q _demand (t)|≤ε·Q _demand (t) Among them, Q _ice.st is the total ice storage capacity, Q _tank is the cooling capacity of the ice tank; Q _tank (t) is the cooling capacity of the ice tank at t; h ₁ , h ₂ are fitted according to actual engineering data; Q _demand ( t) is the cooling load demand at the end of the building at t; ε is the accuracy range to meet the cooling load demand, Q _c (t) is the cooling capacity of the chiller, Q _n (k) is the rated cooling capacity, and PLR (k) is k Part load rate of the chiller. 9. The method for predicting and distributing the cooling load demand of an ice storage air conditioner according to claim 1, wherein in step S5, if the current moment is not the last moment of the day to be regulated, the next moment of the day to be regulated is equal to the current moment , and return to step S2 to obtain the predicted value of cooling load demand of each sub-ant colony at the next moment. 10. An ice-storage air-conditioning cooling load demand prediction and distribution system, characterized in that it comprises: Divide the modules, establish the cooling load prediction map of the target building at all times, set the ant colony parameters, and divide the ant colony into several sub-ant colonies; The sorting module uses each sub-ant colony divided by the dividing module to search for the predicted value of cooling load demand, and obtains several search results of the predicted value of cooling load demand at the current moment of the day to be regulated; Sort by utility function value; The analysis module, according to the sorting result of the sorting module, obtains the ants corresponding to the search results of the cooling load prediction value which tends to be centered in the search result of the cooling load prediction value, and updates the pheromone of the ants to the neighbor sub-ant colony, so that a number of sub-ants are set. Co-evolution of the group; after the set number of iterations, stop the ant search, compare the predicted values of cooling load demand at the current moment of the day to be regulated searched by each sub-ant colony, perform variance analysis, and output the optimal forecast of cooling load demand at the current moment of the day to be regulated. value; The fitting module fits the optimal cooling load demand forecast value obtained by the analysis module with a curve to complete the planning of the cooling load demand forecast value at the corresponding time of the target building; The distribution module, if the current moment is the last moment of the day to be regulated, stops the cycle, obtains the predicted value of the total cooling load demand of the target building on the day to be regulated, and realizes the predicted distribution of cooling load demand.

Images